Note: Starting April 27, 2023 updates change from daily to weekly. Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents by age: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description The MD COVID-19 - Confirmed Deaths by Age Distribution data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by designated age ranges. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Age Distribution data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.

In the United States in 2021, the death rate was highest among those aged 85 and over, with about 17,190.5 men and 14,914.5 women per 100,000 of the population passing away. For all ages, the death rate was at 1,118.2 per 100,000 of the population for males, and 970.8 per 100,000 of the population for women. The death rate Death rates generally are counted as the number of deaths per 1,000 or 100,000 of the population and include both deaths of natural and unnatural causes. The death rate in the United States had pretty much held steady since 1990 until it started to increase over the last decade, with the highest death rates recorded in recent years. While the birth rate in the United States has been decreasing, it is still currently higher than the death rate. Causes of death There are a myriad number of causes of death in the United States, but the most recent data shows the top three leading causes of death to be heart disease, cancers, and accidents. Heart disease was also the leading cause of death worldwide.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances.

REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

Annual data on death registrations by single year of age for the UK (1974 onwards) and England and Wales (1963 onwards).

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Number and percentage of deaths, by age group, sex, and place of residence, 1991 to most recent year.

The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the yehttps://healthdata.gov/d/2sz9-6c59ars 1999-2006. These data are available in two separate data sets: one data set for years 1999-2004 with 3 race groups, and another data set for years 2005-2006 with 4 race groups and 3 Hispanic origin categories. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., state, and county), age group (including infants), race, Hispanic ethnicity (years 2005-2006 only), sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes). The data are produced by the National Center for Health Statistics.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Analysis of ‘TABLE III. Deaths in 122 U.S. cities’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ba37e142-60b9-4eff-ad8f-69029ea5b77e on 27 January 2022.

--- Dataset description provided by original source is as follows ---

TABLE III. Deaths in 122 U.S. cities – 2016. 122 Cities Mortality Reporting System — Each week, the vital statistics offices of 122 cities across the United States report the total number of death certificates processed and the number of those for which pneumonia or influenza was listed as the underlying or contributing cause of death by age group (Under 28 days, 28 days –1 year, 1-14 years, 15-24 years, 25-44 years, 45-64 years, 65-74 years, 75-84 years, and ≥ 85 years).

FOOTNOTE: U: Unavailable. —: No reported cases. * Mortality data in this table are voluntarily reported from 122 cities in the United States, most of which have populations of 100,000 or more. A death is reported by the place of its occurrence and by the week that the death certificate was filed. Fetal deaths are not included.

† Pneumonia and influenza.

§ Total includes unknown ages.

--- Original source retains full ownership of the source dataset ---

This table contains 2394 series, with data for years 1991 -1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 3;Income adequacy quintile 2 ...), Age (14 items: At 25 years; At 30 years; At 35 years; At 40 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Probability of survival; Low 95% confidence interval; life expectancy; High 95% confidence interval; life expectancy ...).

The datasets contains year-, state-, gender- and age-group-wise compiled data on the number of drivers killed in road accidents. The different age groups covered in the dataset include less than 18 years, 18 to 25, 25 to 35, 35 to 45, 45 to 60, 60 and above years, along with persons whose age is not known.

This dataset describes drug poisoning deaths at the county level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning from 1999 to 2015.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

United Kingdom UK: Mortality Rate: Adult: Female: per 1000 Female Adults data was reported at 53.693 Ratio in 2014. This records a decrease from the previous number of 53.890 Ratio for 2013. United Kingdom UK: Mortality Rate: Adult: Female: per 1000 Female Adults data is updated yearly, averaging 83.533 Ratio from Dec 1960 (Median) to 2014, with 55 observations. The data reached an all-time high of 111.369 Ratio in 1963 and a record low of 53.693 Ratio in 2014. United Kingdom UK: Mortality Rate: Adult: Female: per 1000 Female Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Health Statistics. Adult mortality rate, female, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old female dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) University of California, Berkeley, and Max Planck Institute for Demographic Research. The Human Mortality Database.; Weighted average;

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

For English, see below Nederland heeft voor het SARS-CoV-2 virus (coronavirus) een endemische fase bereikt en de GGD teststraten zijn per 17 maart 2023 gesloten. Daardoor wordt de data vanaf 1 april 2023 niet meer bijgewerkt. Bestand vanaf week 40, 2021: COVID-19_casus_landelijk Bestand tot en met week 39, 2021: COVID-19_casus_landelijk_tm Dit bestand wordt vanaf versie 5 niet meer geüpdatet (zie hieronder) Beschikbare formaten: .csv en .json Bronsysteem: OSIRIS Algemene Infectieziekten (AIZ) Beschrijving bestand: Dit bestand bevat de volgende karakteristieken per positief geteste casus in Nederland: Datum voor statistiek, Leeftijdsgroep, Geslacht, Overlijden, Week van overlijden, Provincie, Meldende GGD Het bestand is als volgt opgebouwd: Een record voor elke laboratorium bevestigde COVID-19 patiënt in Nederland, sinds het begin van de pandemie. Vanaf 11 juli 2022 is deze data opgesplitst (zie beschrijving versie 5). Alleen het bestand vanaf week 40, 2021 wordt iedere dinsdag en vrijdag om 16:00 ververst, op basis van de gegevens zoals op 10:00 uur die dag geregistreerd staan in het landelijk systeem voor meldingsplichtige infectieziekten (Osiris AIZ). Het historische bestand (tot en met week 39, 2021) wordt vanaf 11 juli niet meer geüpdatet. Beschrijving van de variabelen: Version: Versienummer van de dataset. Wanneer de inhoud van de dataset structureel wordt gewijzigd (dus niet de dagelijkse update of een correctie op record niveau), zal het versienummer aangepast worden (+1) en ook de corresponderende metadata in RIVMdata (https://data.rivm.nl). Versie 2 update (20 januari 2022): - In versie 2 van deze dataset is de variabele ‘hospital_admission’ niet meer beschikbaar. Voor het aantal ziekenhuisopnames wordt verwezen naar de geregistreerde ziekenhuisopnames van Stichting NICE (data.rivm.nl/covid-19/COVID-19_ziekenhuisopnames.html). Versie 3 update (8 februari 2022) - Vanaf 8 februari 2022 worden de positieve SARS-CoV-2 testuitslagen rechtstreeks vanuit CoronIT aan het RIVM gemeld. Ook worden de testuitslagen van andere testaanbieders (zoals Testen voor Toegang) en zorginstellingen (zoals ziekenhuizen, verpleeghuizen en huisartsen) die hun positieve SARS-CoV-2 testuitslagen via het Meldportaal van GGD GHOR invoeren rechtstreeks aan het RIVM gemeld. Meldingen die onderdeel zijn van de bron- en contactonderzoek steekproef en positieve SARS-CoV-2 testuitslagen van zorginstellingen die via zorgmail aan de GGD worden gemeld worden wel via HPZone aan het RIVM gemeld. Vanaf 8 februari wordt de datum van de positieve testuitslag gebruikt en niet meer de datum van melding aan de GGD Versie 4 update (24 maart 2022): - In versie 4 van deze dataset zijn records samengesteld volgens de gemeente herindeling van 24 maart 2022. Zie beschrijving van de variabele Municipal_health_service voor meer informatie. Versie 5 update (11 juli 2022): - Vanaf 11 juli 2022 is deze dataset opgesplitst in twee delen. Het eerste deel bevat de data vanaf het begin van de pandemie tot en met 3 oktober 2021 (week 39) en bevat ‘tm’ in de bestandsnaam. Deze data wordt niet meer geüpdatet. Het tweede deel bevat de data vanaf 4 oktober 2021 (week 40) en wordt iedere werkdag geüpdatet. Versie 6 update (1 september 2022): - Vanaf 1 september 2022 wordt het tweede deel van de data (vanaf week 40 2021) niet meer iedere werkdag geüpdatet, maar op dinsdagen en vrijdagen. De data wordt op deze dagen met terugwerkende kracht bijgewerkt voor de andere dagen. Versie 7 update (3 januari 2023): - Per 1 januari 2023 verzamelt het RIVM geen aanvullende informatie meer. Dit heeft als gevolg dat we vanaf 1 januari 2023 geen overlijdens meer rapporteren en worden de kolommen [Deceased] en [Week of Death] niet meer aangevuld. Date_file: Datum en tijd waarop de gegevens zijn gepubliceerd door het RIVM Date_statistics: Datum voor statistiek; eerste ziektedag, indien niet bekend, datum lab positief, indien niet bekend, melddatum aan GGD (formaat: jjjj-mm-dd) Date_statistics_type: Soort datum die beschikbaar was voor datum voor de variabele "Datum voor statistiek", waarbij: DOO = Date of disease onset : Eerste ziektedag zoals gemeld door GGD. Let op: het is niet altijd bekend of deze eerste ziektedag ook echt al Covid-19 betrof. DPL = Date of first Positive Labresult : Datum van de (eerste) positieve labuitslag. DON = Date of Notification : Datum waarop de melding bij de GGD is binnengekomen. Agegroup: Leeftijdsgroep bij leven; 0-9, 10-19, ..., 90+; bij overlijden <50, 50-59, 60-69, 70-79, 80-89, 90+, Unknown = Onbekend Sex: Geslacht; Unknown = Onbekend, Male = Man, Female = Vrouw Province: Naam van de provincie (op basis van de verblijfplaats van de patiënt) Deceased: Overlijden. Unknown = Onbekend, Yes = Ja, No = Nee. Vanaf 1 januari 2023 is deze kolom leeg. Week of Death : Week van overlijden. YYYYMM volgens ISO-week notatie (start op maandag t/m zondag). Vanaf 1 januari 2023 is deze kolom leeg. Municipal_health_service: GGD die de melding heeft gedaan. Vanaf 24 maart 2022 is dit bestand samengesteld volgens de gemeente indeling van 24 maart 2022. Gemeente Weesp is opgegaan in gemeente Amsterdam. Met deze indeling is de veiligheidsregio Gooi- en Vechtstreek kleiner geworden en de veiligheidsregio Amsterdam-Amstelland groter; GGD Amsterdam is groter geworden en GGD Gooi- en Vechtstreek is kleiner geworden (https://www.cbs.nl/nl-nl/onze-diensten/methoden/classificaties/overig/gemeentelijke-indelingen-per-jaar/indeling-per-jaar/gemeentelijke-indeling-op-1-januari-2022). -------------------------------------------------------------------------------- Covid-19 characteristics per case, nationwide The Netherlands has reached an endemic phase for the SARS-CoV-2 virus (coronavirus) and the PHS testing facilities will be closed as of March 17, 2023. As a result, the data will no longer be updated from 1 April 2023. File from week 40, 2021: COVID-19_case_landelijk File up to and including week 39, 2021: COVID-19_casus_landelijk_tm This file will no longer be updated from version 5 (see below) Available formats: .csv and .json Source system: OSIRIS General Infectious Diseases (AIZ) File description: This file contains the following characteristics per positively tested case in the Netherlands: Date for statistics, Age group, Gender, Death, Week of death, Province, Notifying PHS The file is structured as follows: A record for every lab-confirmed COVID-19 patient in the Netherlands since the start of the pandemic. From July 11, 2022, this data has been split (see description version 5). Only the file from week 40, 2021 onwards will be updated every Tuesday and Friday at 4:00 PM, based on the data as registered at 10:00 AM that day in the national system for notifiable infectious diseases (Osiris AIZ). The historical file (up to and including week 39, 2021) will no longer be updated from July 11, 2022. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (https://data.rivm.nl). Version 2 update (January 20, 2022): - In version 2 of this dataset, the variable 'hospital_admission' is no longer available. For the number of hospital admissions, reference is made to the registered hospital admissions of the NICE Foundation (data.rivm.nl/covid-19/COVID-19_ziekenhuis Admissions.html). Version 3 update (February 8, 2022) - From 8 February 2022, positive SARS-CoV-2 test results will be reported directly from CoronIT to the RIVM. The test results of other test providers (such as Testing for Access) and healthcare institutions (such as hospitals, nursing homes and general practitioners) that enter their positive SARS-CoV-2 test results via the Reporting Portal of GGD GHOR are also reported directly to the RIVM. Reports that are part of the source and contact investigation sample and positive SARS-CoV-2 test results from healthcare institutions that are reported to the PHS via healthcare email are reported to the RIVM via HPZone. From 8 February 2022, the date of the positive test result is used and no longer the date of notification to the PHS. Version 4 update (March 24, 2022): - In version 4 of this dataset, records are compiled according to the municipality reclassification of March 24, 2022. See description of the Municipal_health_service variable for more information. Version 5 Update (July 11, 2022): - As of July 11, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every working day. Version 6 update (September 1, 2022): - From September 1, 2022, the second part of the data (from week 40 2021) will no longer be updated every working day, but on Tuesdays and Fridays. The data is retroactively updated on these days for the other days. Version 7 update (January 3, 2023): - As of 1 January 2023, the RIVM will no longer collect additional information. As a result, we will no longer report deaths from January 1, 2023 and the [Deceased] and [Week of Death] columns will no longer be completed. Date_file: Date and time when the data was published by the RIVM Date_statistics: Date for statistics; first day of illness, if not known, date of positive lab result, if not known, reporting date to PHS (format: yyyy-mm-dd) Date_statistics_type: Type of date that was available for date for the "Date for statistics" variable, where: DOO = Date of disease onset : First day of illness as reported by PHS. Please note: it is not always known whether this first day of illness actually concerned Covid-19. DPL = Date of first Positive Lab result : Date of the (first) positive lab result. DON = Date of Notification : Date on which the

2020 - 2022, county-level U.S. stroke death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and sex.Visit the CDC Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I60-I69; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall    AIA - American Indian and Alaska Native, non-Hispanic    ASN - Asian, non-Hispanic    BLK - Black, non-Hispanic    HIS - Hispanic NHP – Native Hawaiian or Other Pacific Islander, non-Hispanic MOR – More than one race, non-Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods